Shared-task Self-supervised Learning for Estimating Free Movement Unified Parkinson's Disease Rating Scale III.
Journal:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
PMID:
40039487
Abstract
The Unified Parkinson's Disease Rating Scale (UP-DRS) is used to recognize patients with Parkinson's disease (PD) and rate its severity in clinical settings. Machine learning and wearables can reduce the need for clinical examinations and provide a reliable estimation of the severity of PD at home. This work introduces a multi-channel convolutional neural network to estimate UPDRS part III from motion data recorded by two wearable sensors, considering the gyroscope signals and their spectrogram representations. A novel shared-task self-supervised learning is then employed to leverage the knowledge extracted from the signal to improve the estimation during patients' free-body movements. We utilize 24 PD subjects' data performing daily activities. The estimated UPDRS-III showed an improved correlation with the clinical examinations from 0.67 to 0.81, reducing the mean absolute error from 7.75 to 6.96. Our investigation demonstrates the potential of our approach in providing a reliable estimation of PD severity scores during subjects' daily routines. It can also provide comprehensive information to help physicians manage the disease and adjust the dose and interval of PD medications.